MohammedSaim-Quadri/Intrusion_Detection-System

This project is an Intrusion Detection System (IDS) using machine learning (ML) and deep learning (DL) to detect network intrusions. It leverages the CICIDS2018 dataset to classify traffic as normal or malicious. Key features include data preprocessing, model training, hyperparameter tuning, and Docker containerization for scalable deployment.

26
/ 100
Experimental

This project helps network security analysts evaluate the effectiveness of machine learning systems designed to detect network intrusions. It takes raw network traffic data and classifies it as normal or malicious, but critically, it also shows you how accurately it identifies different types of attacks. Network security and operations teams would use this to understand the true reliability of their intrusion detection systems.

Use this if you need to rigorously test and understand the real-world performance of an intrusion detection system, especially when concerned about its ability to catch rare but critical attacks.

Not ideal if you are looking for a plug-and-play, real-time intrusion detection system ready for immediate live deployment.

network-security threat-detection network-monitoring cybersecurity-analytics security-operations
No Package No Dependents
Maintenance 6 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 0 / 25

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Stars

7

Forks

Language

Python

License

Last pushed

Nov 19, 2025

Commits (30d)

0

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